skip to main content

Search for: All records

Creators/Authors contains: "Tassone, Spencer J."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Coastal marine heatwaves have destructive and lasting impacts on foundational species 13 and are increasing in frequency, duration, and magnitude. High atmospheric temperatures are 14 often associated with marine heatwaves (MHW) which are defined as 5-days of water 15 temperatures above a seasonally varying 90th percentile threshold. In this study we consider the 16 prevalence of MHW propagation into surficial sediments to cause sediment heatwaves (SHW). 17 Within a shallow, subtidal seagrass meadow in Virginia, USA, sediment temperature was 18 measured at hourly intervals at a depth of 5 cm between June 2020-October 2022 at the meadow 19 edge and central meadow interior. The observed sediment temperature, along with a 29-year 20 record of water temperature and water level was used to develop a sediment temperature model 21 for each location. Modeled sediment temperatures were used to identify sediment heatwaves that 22 may thermally stress belowground seagrass. At both meadow locations, sediment heatwave 23 frequency increased at a rate twice that of MHWs in the average global open ocean, coinciding 24 with a 172% increase in the annual number of SHW days, from 11 to 30 days year-1 between 25 1994-2022. Sediment heatwaves at both meadow locations co-occurred with a MHW 79-81% of 26 the time, with nearly all SHWs having a zero day lag. The top 10% most extreme MHWs and 27 SHWs occurred between November and April when thermal stress to seagrass was unlikely. In 28 June 2015 a SHW co-occurred with an anomalously long duration MHW that was associated 29 with a 90% decline in seagrass from this system, suggesting that SHWs may have contributed to 30 the observed seagrass loss. These results document heatwave propagation across the pelagic-31 sediment interface which likely occur broadly in shallow systems with impacts to critical coastal 32 ecosystem processes and species dynamics. 
    more » « less
    Free, publicly-accessible full text available December 15, 2024
  2. The Association for the Sciences of Limnology and Oceanography (ASLO) sponsors Eco-DAS, which is now in its 30th year. The program aims to unite aquatic scientists, develop diverse collaborations, and provide professional development training opportunities with guests from federal agencies, nonprofits, academia, tribal groups, and other workplaces (a previous iteration is summarized in Ghosh et al. 2022). Eco-DAS XV was one of the largest and most nationally diverse cohorts, including 37 early career aquatic scientists, 15 of whom were originally from 9 different countries outside the United States (Fig. 2). As the first cohort to meet in-person since the COVID-19 pandemic, Eco-DAS participants convened from 5 to 11 March 2023 to expand professional networks, create shared projects, and discuss areas of priority for the aquatic sciences. During the weeklong meeting, participants developed 46 proposal ideas, 16 of which will be further developed into projects and peer-reviewed manuscripts. 
    more » « less
    Free, publicly-accessible full text available July 3, 2024
  3. Abstract

    Determining when a disturbance has occurred, its severity, and when the system recovered, is important to numerous questions in the aquatic sciences. This problem can be conceptualized as the timing and degree of perturbation from a typical state, and when the system returns to that typical state. We present an algorithm for detecting disturbance and recovery designed for high‐frequency time series, e.g., data produced by automated sampling devices in instrumented buoys and flux towers. The algorithm quantifies differences in the empirical cumulative distribution functions of moving windows over reference and evaluation periods, and is sensitive to changes in the mean, variance, and higher statistical moments. Tests on simulated data show it accurately identifies disturbance and recovery. Three case studies illustrate the application of our algorithm in different empirical settings. A case study on dissolved oxygen in a Florida, USA estuary following a hurricane identified the disturbance and recovery 73 d later. A case study on air temperature and net ecosystem exchange in the Florida everglades identified cold snaps coinciding with periods of reduced carbon uptake. A case study on rotifer abundance following zebra mussel invasion in the Hudson River, NY showed rotifer collapse following invasion and recovery over a decade later. Methods such as ours can improve understanding response to disturbance and facilitate comparative and synthetic study of disturbance impacts across ecosystems.

    more » « less